WO 2015/028283 A1 discloses a fall detection system and a fall detection method, wherein the system comprises a wearable or portable user device configured to be worn or carried by a user, the user device comprising a proximity sensor for measuring the proximity of the user device to the ground or the floor; and a movement sensor for measuring the movements of the user; the fall detection system further comprising a processing unit configured to process the measurements from the movement sensor to detect a potential fall; activate the proximity sensor if a potential fall is detected; and process the measurements from the proximity sensor to determine if the user has fallen.
In accordance with an exemplary embodiment of WO 2015/028283 A1, the proximity sensor is arranged for measuring changes in capacitance or inductance to estimate the proximity to an object. Changes in the electric or magnetic field of a capacitive or inductive proximity sensor, respectively, can indicate whether an object is in proximity to the sensor.
Fall events affect a huge number of people each year and result in significant injuries, particularly among the elderly. In fact, it has been estimated that falls are one of the top causes of death in elderly people. A fall may be defined as a sudden, uncontrolled and unintentional downward displacement of the body to the ground, followed by an impact, after which the body typically stays down on the ground. In case the fallen person is still fully conscious, he or she may push an emergency switch to ask for help. However, quite often patients are somewhat disoriented or even injured their heads and/or arms so that no deliberate alarm call may be initiated.
Therefore, automatic fall detection systems have been proposed. Conventional automatic motion detection devices are widely known and utilized in patient care environments, more particularly in the context of elderly care and geriatric care. As used herein, motion detection devices include, but are not limited to, fall detection devices. Fall detection devices may also be used in the field of child care, animal care, etc. Fall detection devices may be utilized for sleep monitoring, but also for activity monitoring.
In respect of the fall detection as such, there are several basic technical principles. Currently, fall detection systems can be divided into three main classes: body-worn, ambient-based, and a combination of the latter two.
For instance, fall detection devices are known that simply detect an impact when a monitored subject hits the ground or floor. These devices may implement conventional contact switches, for instance. Further, acceleration based devices are known that basically sense a fall event when a defined (e.g., fall acceleration or speed) kinematic threshold is met. Also combinations thereof may be envisaged.
In accordance with one type, fall detection devices are arranged as body-wearable devices. However, also stationary (remote) fall detection devices are known that may utilize optical sensing. Further, also combinations of body-wearable units and stationary units may be envisaged which may of course cause considerable manufacturing and operating efforts. Moreover, standard fall detection devices often show poor performance when discriminating real fall events from deliberate movements of the monitored subject, for instance quick movements of body limbs. Further, also relative movements between the detection device and an attachment portion (e.g. the wrist) of the monitored subject may have an adverse effect on performance, accuracy, and reliability. When the fall detection device slips out of position, a pseudo-movement may be detected, this may trigger a false alert.
Body-worn fall detection systems typically make use of on-board inertial sensors such as tri-axial accelerometers, gyroscopes, and suchlike. A combination of features derived from these signals is used to detect whether a fall has occurred at any given moment. Optionally, other sensors such as a pressure sensor can be employed to detect height changes.
Ambient-based approaches to fall detection typically employ a number of sensors placed throughout a coverage area of relevance such as the subject's premises or the clinical environment such as a hospital. These sensors include, but are not limited to video cameras and passive infrared (PIR) sensors.
Further, simple leash-based devices are known which are arranged as breakaway detection devices and may detect undesired removals from a defined location or range which triggers an alert. However, these devices are often experienced as considerably uncomfortable, error-prone and also somewhat motion restricting. Further, a breakaway-based fall detection device is basically not capable of discriminating a (horizontal) moving-away event from a (vertical) fall event.
It has been observed that sufficiently high accuracy rates may be obtained for sensors worn basically proximate to the trunk/torso and/or head of the monitored subject. The advantage of such locations is that it is symmetric (no bias due to brain function lateralization, i.e. right/left-handedness) and mainly dominated by larger and slower movements such as walking and changing postures. In particular, a combination of features based on height change and impact can exhibit better discriminative performance.
Challenges may arise when implementing such a fall detection system in a wrist-worn device. The wrists/arms of the subject are prone to faster movements and tend to undergo a larger number of pushes, impacts during daily life than the subject's torso (e.g. reaching for a twisting doorknob). Furthermore, unlike a sensor that is located on the torso or head, the posture of a person after a potential fall often cannot be reliably detected from a wrist-worn device solely employing inertial sensors due to the many degrees of freedom and position of the wrist with respect to the body.
Therefore, another approach to fall detection is to use information about the device's proximity to a surface in conjunction with inertial (acceleration) sensor signal features. After a person has fallen and an impact is detected from the accelerometer signal, or when the person (still) is falling, proximity or changes of proximity to a surface can be measured, where this surface is assumed to be the ground or floor.
There is still a need for improved or alternative motion and fall detection devices, algorithms, and related methods that further enhance the detecting reliability and that are applicable to wrist-worn monitoring systems.